Testing code
Testing code for the final project
To accomplish:
-Looking through the datasets we have available -Need Google Trend data for search terms such as “Cruz” vs “Rourke” or “midterm election” to see if number of searches correlate with # votes for each candidate or # registered voters -What kind of analysis do we want to see -Review the sample final projects
Some links to keep in mind: https://www.cnn.com/election/2018/exit-polls/texas/senate https://www.texastribune.org/2018/10/31/ut-tt-poll-texans-say-immigration-border-security-top-issues/
<<<<<<< HEADlibrary(tidyverse)## ── Attaching packages ────────────────────## ✔ ggplot2 3.0.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.6
## ✔ tidyr 0.8.1 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0## ── Conflicts ──── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(rvest)## Loading required package: xml2##
## Attaching package: 'rvest'## The following object is masked from 'package:purrr':
##
## pluck## The following object is masked from 'package:readr':
##
## guess_encodinglibrary(httr)
library(ggridges)##
## Attaching package: 'ggridges'## The following object is masked from 'package:ggplot2':
##
## scale_discrete_manualscraping election results from the web
New York Times voting results by county
<<<<<<< HEADurl = "https://www.nytimes.com/elections/results/texas-senate"
nytimes_data = read_html(url, col_types = "ccdd")
nytimes_data## {xml_document}
## <html lang="en" itemscope="" xmlns:og="http://opengraphprotocol.org/schema/" itemtype="http://schema.org/NewsArticle">
## [1] <head>\n<title>Texas Senate Election Results: Beto O’Rourke vs. Ted ...
## [2] <body class="eln-race-page eln-2018-11-06 eln-forecast">\n<script ty ...nytimes_data %>%
html_nodes(css = "table")## {xml_nodeset (2)}
## [1] <table class="eln-table eln-results-table">\n<thead><tr>\n<th class= ...
## [2] <table class="eln-table eln-county-table">\n<thead><tr>\n<th class=" ...This seems to have created two tables from the website data.
table_overall = (nytimes_data %>% html_nodes(css = "table")) %>%
.[[1]] %>%
html_table()This seems to have created two tables from the website data.
>>>>>>> 251a2d6ca7820e32c1c5aee20c1fd898c8489465Tidying overall table for exploratory analysis
Bar Plot of Votes per Candidate
This plot illustrates how it was a close race between the top two candidates, O’Rourke and Cruz. As Dikeman had very few votes, we decided to omit Dikeman from further analyses.
Made the first table that which we have final results for the state of texas.
Tidying county table
Made the second table which has all of the 254 county level data for Texas!
Plots for all counties
comparing these county level election results to highly searched voter election interests in google
-using search terms “Midterms” and selecting dataset from top result
<<<<<<< HEADdistrict_searches = read_csv(file = "./data/Search_Data_US_Congressional_District_26Sep2018.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## District = col_character(),
## Code = col_character(),
## State = col_character(),
## FIRST = col_character(),
## SECOND = col_character(),
## THIRD = col_character(),
## FOURTH = col_character(),
## FIFTH = col_character(),
## SIXTH = col_character(),
## SEVENTH = col_character(),
## EIGHTH = col_character(),
## NINTH = col_character(),
## TENTH = col_character(),
## `Maternity leave in the United States` = col_integer(),
## `Single-payer healthcare` = col_integer(),
## `Tax Cuts and Jobs Act of 2017` = col_integer(),
## `Transgender people in the military` = col_integer()
## )## See spec(...) for full column specifications.TX_searches =
district_searches %>% janitor::clean_names() %>%
filter(state == "TX")
TX_counts =
TX_searches %>%
count(fifth)
#the topics that were searched most were health care, immigration, mental health, united nations
#second most searched: immigration, health care, Medicare, Medicaid, capital punishment
#third most searched: Medicare, Medicaid, September 11 attacks, immigration,
#fourth most searched: Medicaid, Medicare, Immigration...
#fifth: Medicaid, mental health, Medicare, September 11 attacks...
#Thus, we should focus on the variables health care, immigration, Medicare, Medicaid, Mental health, September 11 attacksuploading county congressional district txt file
congress_district = read.csv(file = "./data/congress_district2.csv")
congress_district$county_name = str_replace(congress_district$county_name,"Sterlin", "Sterling")
congress_district$county_name = str_replace(congress_district$county_name,"MuCulloch","McCulloch")
#misspelled counties discovered while exploring data when merging later onTX_searches =
TX_searches %>%
separate(code, into = c("remove_1", "district_num"), sep = "-") %>%
mutate(district_num = as.numeric(district_num)) %>%
select(district_num, most_searched = first, x2003_invasion_of_iraq:womens_health)
nested_congress =
congress_district %>%
nest(county_name)
merged_searches= merge(TX_searches, nested_congress, by="district_num", all=TRUE) %>%
unnest()
#rename variable county_name
merged_searches = merged_searches %>%
select(district_num, county=county_name, everything())
merged_nyt_searches = merge(merged_searches, table_county, by= "county", all=TRUE)